Big Data Analysis Technology Research Articles

Информационные системы учета и анализа данных жилищно-коммунального хозяйства многоквартирных домов

The article discusses the development of information systems of housing and communal services of the Russian Federation from the point of view of resource consumption data accounting and analysis. The object of the study is "smart" apartment buildings. The subject of the research is information platforms, information systems and individual software developments for monitoring resource consumption in apartment buildings. It is noted that the digitalization of housing and communal services is just beginning and mainly refers to new construction. There is a potential for using digital services to manage a "smart" apartment building, including for the organization of telemetry. Three levels of development of digital services for resource management are identified. The list of the main digital platforms supporting the telemetry function in apartment buildings is presented. The possibilities of integration of various automated information systems are discussed. It is noted that the analytical processing of housing and communal services information is focused on the traditional tasks of management companies, such as the formation of statistics and reporting on resource consumption. It is proposed to pay attention to machine learning methods and intelligent big data analysis technologies.

Retracted: Problems and Countermeasures of China’s International Trade in Agricultural Products under the Belt and Road Strategy Based on Big Data Analysis Technology in the Internet of Things Era

Retracted: Problems and Countermeasures of China’s International Trade in Agricultural Products under the Belt and Road Strategy Based on Big Data Analysis Technology in the Internet of Things Era

Development of Big Data Analytics Technology on Sea Freight Operational Performance Using the Agile Model

The development of industry 4.0 is still difficult for a country to produce all goods according to the required needs. Big data analytics technology has the potential to revolutionize how logistics companies, including freight forwarders, can facilitate real-time monitoring in the supply chain by increasing flexibility. Management information systems produce a number of data from different sources and in different formats according to business needs. The development of big data analytics uses a dynamic system development method or model (DSDM), which is a dynamic system development model that uses a rapid application development (RAD) approach. Software development with an emphasis on collaboration, customer engagement, and continuous delivery of working software. This model is very suitable for the development of big data analytics technology that can provide solutions that often involve complex and rapidly changing requirements with large amounts of data and the business need to provide feedback from stakeholders in real-time. It is hoped that the final results of this research will provide insight into sea freight operations that are more informative and efficient for the logistics industry in Indonesia.

Application of Big Data in Entrepreneurship and Innovation Education for Higher Vocational Teaching

The traditional grid did not consider the dynamic characteristics of the big data of innovation and entrepreneurship education. The grid based quantitative evaluation model of analytical AI teaching information based on adaptive identification and weighting algorithm is gradually applied to the daily operating system of innovation and entrepreneurship education. This article studies the application of adaptive recognition weighting algorithm in grid analysis of innovation and entrepreneurship education in domestic vocational colleges, and proposes an AI teaching model of grid analysis based on adaptive recognition weighting algorithm and online analysis of innovation and entrepreneurship education intelligence in colleges and universities. The results show that the innovation and entrepreneurship education model in colleges and universities based on grid analysis network teaching and adaptive recognition weighting algorithm can efficiently and intelligently diagnose students' teaching data, and achieve the innovation of big data analysis technology in colleges and universities.

Research on the Application of Big Data Analysis Platform in the Enterprise Management Optimization

As companies attempt to make analysis an important data support component of daily decision-making, big data analysis technology is rapidly expanding across all industries. Although there are many software tools and libraries available to assist analysts and software engineers in developing solutions, enterprises are looking for reliable analysis platforms that can meet their specific goals and requirements. In order to minimize costs, such platforms also need to coexist with existing IT infrastructure and reuse the knowledge and resources already accumulated within the organization. To meet these requirements, this article proposes the Data Analysis Solution Engineering (DASE) framework - a knowledge driven approach supported by semantic web technology, for the design and development of requirements engineering and new data analysis platforms. It includes capturing data analysis platform requirements through a knowledge base, and enterprises learning how to use this data analysis platform to analyze all daily production data involved in the engineering data analysis platform. This article analyzes the DASE framework through knowledge modeling, requirement modeling, data architecture modeling, and platform design modeling, and demonstrates how it promotes knowledge and requirement driven data analysis platform engineering. The resulting data analysis platform is considered user-friendly, easy to maintain, and flexible in handling changes in requirements. This work contributes to the knowledge system of knowledge driven requirements engineering and data analysis platform engineering by providing customized models and reference architectures for different analytical application fields.

Modelling simulation of college vocal music teaching development path based on big data analysis

Abstract This paper uses big data analysis to determine user similarity and calculate the bias in selecting nearest neighbors. The temporal factor is introduced to fully reflect the changing status of users’ interest degrees so that the recommendation accuracy can be significantly improved. According to the nearest neighbors’ rating of experimental teaching resources, the collected data on the effectiveness of vocal music teaching in colleges and universities are clustered, and the results are reflected using degree weights and biases for updating. It was discovered that seven samples had actual student vocal rating values above 0.5, and the overall vocal test scores could reach 86 or higher. To be more energetic in contemporary times, vocal music teaching in colleges and universities should be reformed and innovated to incorporate big data analysis technology.

Big Data in Medical Field

Large volumes of structured, unstructured, and semi-structured data have been produced in recent years by numerous global institutions; this heterogeneous data is collectively known as big data. Because several sources are known to provide large volumes of heterogeneous data, the health industry sector has to deal with the challenge of managing big data. In the healthcare industry, numerous big-data analytics technologies and approaches have been created to manage these enormous volumes of data. The influence of big data on healthcare, as well as the range of applications and technologies available within the Hadoop ecosystem, will all be covered in this paper. The fragmentation of healthcare data, ethical concerns, usability challenges, security and privacy concerns, and other issues are some of the barriers preventing the successful application of big data in healthcare, according to this research.

The importance of Big Data Analytics technology in business management

The importance of Big Data Analytics technology in business management

A Study of Folk Aesthetic Culture of Minnan Ancestral Hall in the Context of Big Data and Artificial Intelligence

In the past, people with the same family name gathered in a region were made into a family, and each family had its family temple for worshipping ancestors and remembering their forefathers, and such family temples with special significance were called ancestral halls. The southern Min region has experienced more hardships compared to the northern region, so it pays more attention to the family and places an important place on the transmission of blood ties. The values of people are subtly changed in this environment, and through the inculcation of generations, the people of southern Fujian internalized the values inherited from this architecture of ancestral halls into their aesthetic consciousness. The purpose of this study is to explore the impact of technological development on the evolution of regional aesthetic characteristics in the context of Big data and Artificial intelligence, starting from the folk aesthetic culture of Minnan ancestral halls. The architecture of Minnan ancestral halls is characterized by the adaptation of ancestral hall architecture to the climate of Southern Fujian, the unique architectural framework of ancestral halls, the use of vibrant colors in Minnan ancestral halls, and the Minnan ancestral halls. The art of stone carving and aesthetic literacy are two major aesthetic features of Minnan's ancestral halls. The number of Minnan ancestral halls is huge, and to analyze their aesthetic characteristics it is necessary to use the ability of Big data storage to analyze massive data and to extract some representative ancestral hall buildings from the database of Minnan ancestral halls for analysis. The various aesthetic features of the database are clustered and analyzed through Big data analysis technology to summarize the representative aesthetic features of Minnan ancestral halls. At the same time, some people in the Minnan area were selected as the research subjects, and the data related to the folk aesthetic consciousness of the people were obtained using a questionnaire survey, and then the "aesthetic elements" and "aesthetic consciousness" were input into the artificial intelligence training model by combining Big data and Artificial intelligence technology. The relationship between elements is explored through artificial intelligence decision-making. Finally, this conclusion is illustrated in four dimensions. From the dimension of color, the residents of Minnan mostly use relatively bright colors to decorate their houses, which is consistent with the use of red and black colors in Minnan's ancestral halls. In terms of shape, the architectural style of Minnan ancestral halls also influenced the development of later buildings, some of which evolved from Minnan ancestral halls as prototypes. In terms of materials, the Minnan ancestral halls were built with a combination of wood and stone, while later generations only increased the proportion of stone. In terms of culture, the development of folk aesthetic consciousness is echoed by the core values of blood and clan ties transmitted by the Minnan ancestral halls left.

Path identification and effect assessment of digital economy-driven manufacturing quality development in the context of big data analysis

Abstract This paper uses big data analysis technology to construct a digital intelligent manufacturing system. Firstly, the K-mean algorithm is used to cluster the enterprise manufacturing data, and then the fuzzy C-mean algorithm is combined to detect the abnormal data and realize the preferential selection and control of product features. A semi-parametric algorithm is introduced to establish index weights to achieve optimal resource allocation. The results show that after manufacturing enterprises produce through the digital intelligent manufacturing system, qualified products account for 82% of the total output and productivity increases by approximately 44% on average. Big data analysis technology enables enterprises to analyze data effectively and enhances the development of the manufacturing industry in the digital economy.

Retracted: Construction of a Legal System of Corporate Social Responsibility Based on Big Data Analysis Technology.

[This retracts the article DOI: 10.1155/2022/8448095.].

Internet of Things and Big Data Analytics for Risk Management in Digital Tourism Ecosystems

Participation and inclusion in the business ecosystem have emerged as a growing trend for company collaboration in areas such as innovation, product development, and research. Collaborations can take many forms, ranging from the traditional value chain to strategic alliances, corporate networks, and digital ecosystems. The Internet of Things (IoT) and Big Data Analytics (BDA) play key roles in developing smart tourism destinations by delivering efficient management solutions, increased public safety, and improved operational efficiency while managing different risks and challenges, while also being a source of such risks and challenges. The objective of this article was to investigate the potential of IoT and BDA to properly control the risks associated with participants in a tourism destination’s digital ecosystem. The authors used the systematic literature review (SLR) method to examine scientific and applied articles on this subject. As a result, the main risks of the digital tourism ecosystem (DTE) as a whole and of the IoT and BDA technologies used in it were identified and classified; the features of DTE that affect risk management in it were distinguished; IoT technologies and their applications used in DTE were outlined; and the roles of DTE participants and the possible IoT technologies that can successfully address the risks associated with a given role were defined.

Retracted: Evaluation Method of Advanced Mathematics Teaching Reform Effect Based on Big Data Analysis Technology

Retracted: Evaluation Method of Advanced Mathematics Teaching Reform Effect Based on Big Data Analysis Technology

Retracted: Evaluation Method of Comprehensive Quality of Environmental Protection Teachers Based on Big Data Analysis Technology.

[This retracts the article DOI: 10.1155/2022/6848352.].

A mechanism-data hybrid-driven framework for identifying dynamic characteristic of actual chemical processes

As a prerequisite of accurate process analysis, prediction and optimization, the precise identification of process dynamic characteristics is of great significance. Traditional identification methods and data-driven technologies suffer from low accuracy and noise interference when facing the identification of actual chemical process. In this contribution, a mechanism-data hybrid-driven framework is developed to simultaneously capture the delay time and dynamic response of process variables. Combining non-parametric identification method, theoretical model and big data analysis technology, the proposed framework is able to derive more accurate and reliable identification results from the distributed control system (DCS) data directly, avoiding drawbacks brought by single method. The presented framework successfully identifies the flowrate dynamic properties of an actual deethanization process, and outperforms traditional approaches including finite impulse response model and Welch's averaged periodogram method. The contribution is significant for dynamic characteristic identification of actual chemical process, facilitating the developments of high-accuracy models for process prediction, control and intelligent optimization.

Achieving competitive advantage through technology-driven proactive supply chain risk management: an empirical study

AbstractWhilst there has been previous work focused on the role of technologies in enhancing supply chain risk management and, through such an enhancement, increased competitive advantage, there is a research gap in terms of understanding the links between external institution pressures and internal adoption factors. We use institutional theory (IT) and the resource based view (RBV) of the firm to address this gap, developing a framework showing how a proactive technology-driven approach to supply chain risk management, combining both external with internal factors, can result in competitive advantage. We validate the framework through analysis of quantitative data collected via a survey of 218 firms in the manufacturing and logistics industry sectors in India. We specifically focus on the technologies of track-and-trace (T&T) and big data analytics (BDA). Our findings show that firms investing in T&T/BDA technologies can gain operational benefits in terms of uninterrupted information processing, reduced time disruptions and uninterrupted supply, which in turn gives them competitive advantage. We add further novelty to our study by demonstrating the moderating influences of organisational culture and flexibility on the relationship between the technological capabilities and the operational benefits.

Research on the innovation of ideological and political education for college students based on the Civic and Political Science Cloud Education Platform

Abstract Innovative ideological and political education for college students is to help better students improve their ideological quality and political literacy. In this paper, under the background of big data, the Civic and Political Cloud Education Platform is constructed using big data technology, which is mainly composed of four main parts: model layer, view layer, control layer, and basic services. Student behavior data is collected and analyzed through a collaborative filtering recommendation algorithm under big data collection and analysis technology, and performance simulation analysis is conducted for the Civic and Political Science Cloud Education Platform. From the performance analysis, when the number of concurrent users of the Civic Education Cloud Platform is 80-120, the average transaction processing per second is stable at 10.5 s. From the practicality, the Civic Education Cloud Platform built by big data can effectively explore the value of Civic Education teaching resources. This shows that the Civic and Political Science cloud education platform is an effective way to innovate and develop the ideological and political education of college students, and through this platform, the ideological quality and political literacy of college students can be effectively improved.

Searchable encryption algorithm in computer big data processing application

With the continuous development of computer technology, the amount of data has increased sharply, which has promoted more and more diversified data transportation and processing methods. At the same time, computer data analysis technology can effectively process data. This is reflected in the computer big data analysis technology not only can realize data visualization analysis, but also has data prediction and data quality management. The development of cloud computing network technology can not only provide convenience points for individuals, but also provide space for enterprises to store data. The emergence of keyword search encryption algorithms solves this problem. When users use keywords to search encryption algorithms, they can search for cipher text keywords to find the files or data they want in the cloud environment. At present, it has been widely used. In addition, this article also improves the keyword search plan and the user's query plan according to the dynamic changes of keywords, and proposes a user's multi-dynamic keyword search encryption plan. Through this program, users can search for encrypted files by keywords and change them, and the changed data will be dynamically updated. In this way, the program can realize multi-user data sharing, and can realize efficient search and dynamics.

Research on Space Image Fast Classification Based on Big Data

In order to improve the accuracy and effect of space image classification, the author proposes a space image classification method based on Big data analysis, aiming at the shortcomings of low accuracy and long time of current image classification. First, analyze the current research progress of image classification, find out the shortcomings of different classification methods, then collect aerospace images, preprocess the images, and use big data analysis technology to establish image classifiers, image classification was performed using an image classifier, and finally simulation experiments were conducted with other methods for image classification. The results indicate that: The average classification time of this method for aerospace images is 3.5 minutes, which saves 14 minutes and 29 minutes compared to traditional method 1 and traditional method 2, respectively. This indicates that this method has the shortest image classification time and improves the classification efficiency of aerospace images. This method has been proven to have high accuracy in image classification, the shortest classification time, and significant advantages compared to other image classification methods.

Unfolding the link between big data analytics and supply chain planning

Big data analytics (BDA) has captured growing research interests in operations and supply chain management literature, yet, despite the significant implication, extant knowledge falls short in drawing the link between BDA and supply chain planning (SCP) with in a structured manner. This paper employs the Delphi technique to uncover the synergies between BDA technology, conceptualized as big data sources and BDA methods, and the SCP activities framed with the SCP matrix. The panel runs for three rounds with 35 experts including scholars, supply chain practitioners, and BDA specialists. The results of this paper suggest that the relevance of BDA depends on the focal SCP activity. Thirty-five projections are presented on the expected impact of BDA on SCP that are classified into three groups based on the significance of impact and probability of occurrence. This work advances the understanding of BDA in supply chain management drawing implications to prioritize BDA investment for SCP.